Uncovering new toxicities from chronic non-rodent studies
Preclinical toxicology studies are an essential part of the drug discovery-development pipeline, to support the safe conduct of clinical trials. And drug safety is, of course, one of the most critical aspects to ensure during drug development.
We were pleased to see the recent publication by Merck on a text-mining approach to assess the value of chronic non-rodent toxicology studies.
Preclinical safety assessment groups employ a variety of animal models and assays to satisfy regulatory agency requirements to identify and characterize drug toxicities, describe drug exposures, and provide qualitative and quantitative risk assessments for human exposure. These require considerable resource investment, however the results are often “locked away” in internal reports. This means re-use of these valuable data is difficult and costly.
This is a common situation within the pharmaceutical industry – where critical information is locked away in textual reports, such as the informed scientific conclusions of pathologists, histologists, safety experts. Natural language processing can overcome the barriers, extracting structured facts from unstructured documents, and Merck’s paper describes an evaluation of a text mining workflow to access these important data.